{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/leveraging-trajectory-prediction-for","title":"Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection","arxiv_id":"2207.02279","date":"2022-07-05","proceeding":null,"authors":["Asiegbu Miracle Kanu-Asiegbu","Ram Vasudevan","Xiaoxiao Du"],"abstract":"Video anomaly detection is a core problem in vision. Correctly detecting and identifying anomalous behaviors in pedestrians from video data will enable safety-critical applications such as surveillance, activity monitoring, and human-robot interaction. In this paper, we propose to leverage trajectory localization and prediction for unsupervised pedestrian anomaly event detection. Different than previous reconstruction-based approaches, our proposed framework rely on the prediction errors of normal and abnormal pedestrian trajectories to detect anomalies spatially and temporally. We present experimental results on real-world benchmark datasets on varying timescales and show that our proposed trajectory-predictor-based anomaly detection pipeline is effective and efficient at identifying anomalous activities of pedestrians in videos. Code will be made available at https://github.com/akanuasiegbu/Leveraging-Trajectory-Prediction-for-Pedestrian-Video-Anomaly-Detection.","url_abs":"https://arxiv.org/abs/2207.02279v1","url_pdf":"https://arxiv.org/pdf/2207.02279v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"leveraging-trajectory-prediction-for","repo_url":"https://github.com/akanuasiegbu/leveraging-trajectory-prediction-for-pedestrian-video-anomaly-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"},{"task_slug":"video-anomaly-detection","task_name":"Video Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.02279","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}